11 Omnicom Group SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

At Omnicom Group, SQL daily to analyze advertising data, and customer behavior trends to strategize marketing campaigns. Because of this, Omnicom Group almost always asks SQL coding questions during interviews for Data Analyst, Data Science, and BI jobs.

11 Omnicom Group SQL Interview Questions

SQL Question 1: Analyze Ad Impressions over different Locations

Omnicom Group, being a global marketing and corporate communications company, runs advertisements across many locations. They keep track of how well these ads are being received in different areas. The dataset they have includes each ad's ID, the location it was shown, the number of impressions and clicks it received.

You are to write a SQL query that calculates the running total of impressions, the running total of clicks and a cumulative click-through-rate (CTR) for each ad per location, ordered by ascending date. Each row’s CTR should be the total clicks up to that point divided by the total impressions to that point, rounded to 2 decimal places.

The CTR is defined as: CTR = (Total Clicks / Total Impressions)

Example Input:
12022-06-01New York10010
12022-06-02New York20020
12022-06-03New York15015
12022-06-01Los Angeles20010
12022-06-02Los Angeles15020
12022-06-03Los Angeles20015
Example Output:
12022-06-01New York100100.10
12022-06-02New York300300.10
12022-06-03New York450450.10
12022-06-01Los Angeles200100.05
12022-06-02Los Angeles350300.09
12022-06-03Los Angeles550450.08

In the above query, a is used with the function to calculate running totals of impressions and clicks partitioned by and . Then it's used again to calculate the cumulative CTR (click through rate) based on the running totals. Finally, the result is ordered by and .

p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 2: Top 3 Salaries

Imagine there was a table of Omnicom Group employee salary data. Write a SQL query to find the top 3 highest paid employees in each department.

Omnicom Group Example Input:

employee_idnamesalarydepartment_id
1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

Example Input:

department_iddepartment_name
1Data Analytics
2Data Science

Example Output:

department_namenamesalary
Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

Write a SQL query for this interview question interactively on DataLemur:

We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

If the code above is hard to understand, you can find a detailed solution here: Top 3 Department Salaries.

SQL Question 3: Can you explain the difference between the and window functions in SQL?

While both and are used to rank rows, the key difference is in how they deal with ties.

RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.

DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.

Suppose we had data on how many deals different salespeople at Omnicom Group closed, and wanted to rank the salespeople.

The result of this query would be:

namedeals_closedrankdense_rank
Akash5011
Brittany5021
Carlos4032
Dave4043
Eve3053
Frank2064

As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.

SQL Question 4: Filtering Omnicom Group's Clients

Omnicom Group, a global media, marketing, and corporate communications holding company, wants to filter out specific records from their customer database.

The following is a sample of the table in their database:

Example Input:
client_idnameindustrycountryactive_account
1254Company AMediaUSAtrue
3490Company CMediaUKtrue
4812Company DCorporate CommunicationsUSAfalse
5837Company EMediaGermanytrue

The company wants to list active accounts ( is true) that are either in the Media industry or based in the USA.

Write a SQL query to filter out the required records.

Here’s the PostgreSQL query to achieve this:

This query returns all columns () from the table where the client account is active () and either the industry of the client is 'Media' or the client is based in the 'USA'. If a row in the table meets these conditions, it is included in the result set.

Example Output:
client_idnameindustrycountryactive_account
1254Company AMediaUSAtrue
3490Company CMediaUKtrue
5837Company EMediaGermanytrue

Fun fact: Omnicom is the first marketing agency to have enterprise access to Open AI GPT models to help them with their client work! Read about how they stay on top of Data and Tech trends and use Gen AI here.

SQL Question 5: Can you list the various types of joins in SQL, and describe their purposes?

In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.

There are four distinct types of JOINs: , , , and .

(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.

LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.

RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.

FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.

SQL Question 6: Average Ad Campaign Duration

As a Data Analyst at Omnicom Group, an advertising firm, one important metric that can provide insights into our operations is the average duration of our advertising campaigns.

We have a table called that stores all the details about each campaign. It has the following columns: (unique identifier for each campaign), (the ID of the client that ordered the campaign), (when the campaign starts) and (when the campaign ends).

Given this table, write a SQL query that returns the average campaign duration for each client that we have worked with.

Example Input:

campaign_idclient_idstart_dateend_date
101A00101/01/201901/02/2019
102A00101/02/201901/04/2019
103A00201/01/201901/03/2019
104A00301/05/201901/08/2019
105A00201/07/201901/10/2019

Example Output:

client_idavg_duration_days
A0012.0
A0022.5
A0033.0

This PostgreSQL query solves the problem by using the function and grouping by the . For each group (i.e., for each client), calculates the average difference between the and across all campaigns for that client. This gives us the average campaign duration (in days) for each client.

To practice a very similar question try this interactive Facebook Advertiser Status Question which is similar for focusing on advertising campaigns or this JPMorgan Chase Card Launch Success Question which is similar for involving analysis of an event's duration.

SQL Question 7: What's a self-join, and when would you use one?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.

For another example, say you were doing an HR analytics project and needed to analyze how much all Omnicom Group employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Omnicom Group employees who work in the same department:

This query returns all pairs of Omnicom Group employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Omnicom Group employee being paired with themselves).

SQL Question 8: How to calculate click-through conversion rates for Omnicom Group?

Omnicom Group, a marketing agency, would like to understand the click-through conversion rates for their different digital ads. We have two tables named and which record actions of users clicking on the ads and adding products corresponding to those ads to their cart respectively.

The table has the following structure:

637101A56706/15/2022 00:00:00
142257B98306/10/2022 00:00:00
839123A56706/18/2022 00:00:00
951424C85207/26/2022 00:00:00
726413B98307/05/2022 00:00:00

The table has the following structure:

529101A56706/16/2022 00:00:00
319257B98306/11/2022 00:00:00
973424C85207/26/2022 00:00:00
723100A56707/07/2022 00:00:00
827413B98307/06/2022 00:00:00

Write a SQL query that calculates the click-through conversion rate for each ad by dividing the number of users who have added a product after clicking the ad by the total number of users who have clicked the ad.

In this SQL query, we join the and tables on and . After joining, we implement a WHERE clause where we only keep the records where the is the same or after the . We then use the GROUP BY clause to aggregate data by . For the conversion rate, we divide the count of distinct users who have added a product after clicking the ad (B.user_id) by the count of distinct users who have clicked the ad on (A.user_id). We use the DISTINCT keyword to count unique users and the CAST function to get a non-integral conversion rate. This calculation is done for each .

To solve a related SQL interview question on DataLemur's free interactive coding environment, try this SQL interview question asked by Facebook:

SQL Question 9: Finding the Maximum Advertising Revenue per Client

Omnicom Group is a global marketing and corporate communications company. Imagine Omnicom Group has an table that tracks advertising revenue from clients. Each row records the , the , the , and the .

The question is: For each year, which client generated the maximum advertising revenue?

Write an SQL query that will return one row for each year, along with the and for the client that contributed the most in that year. If multiple clients have the same maximum revenue in a year, return any one of them.

Example Input:
revenue_idclient_idmonthyearrevenue
110101202050000
210203202060000
310104202070000
410305202180000
510406202190000
Example Output:
yearclient_idmax_revenue
202010170000
202110490000

This query first identifies the maximum revenue for each year (in the subquery named Yearly_Max). It then joins this to the original advert_revenue table (on the conditions that the year and revenue match) to get the associated .

SQL Question 10: Can you describe the concept of a database index and the various types of indexes?

A database index is a data structure that improves the speed of data retrieval operations on a database table.

There are few different types of indexes that can be used in a database:

• Primary index: a unique identifier is used to access the row directly.
• Unique index: used to enforce the uniqueness of the indexed columns in a table.
• Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
• Clustered index: determines the physical order of the data in a table

For a concrete example, say you had a table of Omnicom Group customer payments with the following columns:

Here's what a clustered index on the column would look like:

A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values. This speeds up queries that filter or sort the data based on the , as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of June, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.

SQL Question 11: Filter Client Records From The Database

Omnicom Group has a database of their clients where the company stores various details such as the client's id, name, sector they belongs to, and their email ID. The company wants to find out the list of clients whose email service provider is Gmail. You are given the task to filter the records in their database such that the output consists of only those clients whose email ID contains "gmail".

Example Input:
client_idnamesectoremail_id
6548ABC CorpITabc.corp@gmail.com
3596Acme CorpEcommerceacme.corp@yahoo.com
7853Doe IndustriesManufacturingdoe.industries@gmail.com
4314XYZ LtdRetailxyz.ltd@outlook.com
9576GHI IncHealthcareghi.inc@gmail.com
Example Output:
client_idnamesectoremail_id
6548ABC CorpITabc.corp@gmail.com
7853Doe IndustriesManufacturingdoe.industries@gmail.com
9576GHI IncHealthcareghi.inc@gmail.com

You can use the SQL keyword to solve this problem. is a keyword in SQL that is used with the WHERE clause to search for a specified pattern in a column.

This query will return all records from the 'clients' table where the 'email_id' contains the string 'gmail.com'. The '%' symbol is a wildcard character that matches zero, one, or multiple characters. In this query, it is used to match any characters that may appear before and after 'gmail.com' in the 'email_id' column.

How To Prepare for the Omnicom Group SQL Interview

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the above Omnicom Group SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG tech companies and tech startups.

Each DataLemur SQL question has multiple hints, detailed solutions and crucially, there's an online SQL coding environment so you can right online code up your SQL query answer and have it executed.

To prep for the Omnicom Group SQL interview it is also useful to solve interview questions from other advertising companies like:

However, if your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.

This tutorial covers things like manipulating date/time data and filtering on multiple conditions using AND/OR/NOT – both of these show up frequently in Omnicom Group SQL assessments.

Omnicom Group Data Science Interview Tips

What Do Omnicom Group Data Science Interviews Cover?

In addition to SQL query questions, the other topics covered in the Omnicom Group Data Science Interview are:

How To Prepare for Omnicom Group Data Science Interviews?

The best way to prepare for Omnicom Group Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
• A Refresher covering SQL, AB Testing & ML
• Amazing Reviews (1000+ reviews, 4.5-star rating)